
"""simple docstring"""






import argparse
import json
import os

import fairseq
import torch
from fairseq.data import Dictionary

# Register SEW's fairseq modules
from sew_asapp import tasks  # noqa: F401

from transformers import (
    SEWConfig,
    SEWForCTC,
    SEWModel,
    WavaVecaCTCTokenizer,
    WavaVecaFeatureExtractor,
    WavaVecaProcessor,
    logging,
)


logging.set_verbosity_info()
UpperCAmelCase        =				logging.get_logger(__name__)

UpperCAmelCase        =				{
    """post_extract_proj""": """feature_projection""",
    """encoder.pos_conv.0""": """encoder.pos_conv_embed.conv""",
    """self_attn.k_proj""": """encoder.layers.*.attention.k_proj""",
    """self_attn.v_proj""": """encoder.layers.*.attention.v_proj""",
    """self_attn.q_proj""": """encoder.layers.*.attention.q_proj""",
    """self_attn.out_proj""": """encoder.layers.*.attention.out_proj""",
    """self_attn_layer_norm""": """encoder.layers.*.layer_norm""",
    """fc1""": """encoder.layers.*.feed_forward.intermediate_dense""",
    """fc2""": """encoder.layers.*.feed_forward.output_dense""",
    """final_layer_norm""": """encoder.layers.*.final_layer_norm""",
    """encoder.upsample.0""": """encoder.upsample.projection""",
    """encoder.layer_norm""": """encoder.layer_norm""",
    """w2v_model.layer_norm""": """layer_norm""",
    """w2v_encoder.proj""": """lm_head""",
    """mask_emb""": """masked_spec_embed""",
}




def __magic_name__	(				_lowerCamelCase:		Optional[Any],					_lowerCamelCase:		Tuple,					_lowerCamelCase:		Optional[int],					_lowerCamelCase:		Optional[Any],					_lowerCamelCase:		str			)					->						Tuple:

	'''simple docstring'''




	for attribute in key.split('''.'''			):
		lowerCAmelCase												=  getattr(_lowerCamelCase,					_lowerCamelCase			)

	if weight_type is not None:
		lowerCAmelCase												=  getattr(_lowerCamelCase,					_lowerCamelCase			).shape
	else:
		lowerCAmelCase												=  hf_pointer.shape

	assert hf_shape == value.shape, (
	    F"""Shape of hf {key + "." + weight_type if weight_type is not None else ""} is {hf_shape}, but should be"""
	    F""" {value.shape} for {full_name}"""
	)

	if weight_type == "weight":
		lowerCAmelCase												=  value
	elif weight_type == "weight_g":
		lowerCAmelCase												=  value
	elif weight_type == "weight_v":
		lowerCAmelCase												=  value
	elif weight_type == "bias":
		lowerCAmelCase												=  value
	else:
		lowerCAmelCase												=  value

	logger.info(F"""{key + "." + weight_type if weight_type is not None else ""} was initialized from {full_name}."""			)




def __magic_name__	(				_lowerCamelCase:		Tuple,					_lowerCamelCase:		List[str],					_lowerCamelCase:		str			)					->						Dict:

	'''simple docstring'''




	lowerCAmelCase												=  []
	lowerCAmelCase												=  fairseq_model.state_dict()

	lowerCAmelCase												=  hf_model.sew.feature_extractor if is_finetuned else hf_model.feature_extractor

	for name, value in fairseq_dict.items():
		lowerCAmelCase												=  False
		if "conv_layers" in name:
			load_conv_layer(
			    _lowerCamelCase,					_lowerCamelCase,					_lowerCamelCase,					_lowerCamelCase,					hf_model.config.feat_extract_norm == '''group''',					)
			lowerCAmelCase												=  True
		else:
			for key, mapped_key in MAPPING.items():
				lowerCAmelCase												=  '''sew.''' + mapped_key if (is_finetuned and mapped_key != '''lm_head''') else mapped_key

				if key in name or key.split('''w2v_model.'''			)[-1] == name.split('''.'''			)[0]:
					lowerCAmelCase												=  True
					if "*" in mapped_key:
						lowerCAmelCase												=  name.split(_lowerCamelCase			)[0].split('''.'''			)[-2]
						lowerCAmelCase												=  mapped_key.replace('''*''',					_lowerCamelCase			)
					if "weight_g" in name:
						lowerCAmelCase												=  '''weight_g'''
					elif "weight_v" in name:
						lowerCAmelCase												=  '''weight_v'''
					elif "weight" in name:
						lowerCAmelCase												=  '''weight'''
					elif "bias" in name:
						lowerCAmelCase												=  '''bias'''
					else:
						lowerCAmelCase												=  None
					set_recursively(_lowerCamelCase,					_lowerCamelCase,					_lowerCamelCase,					_lowerCamelCase,					_lowerCamelCase			)
				continue
		if not is_used:
			unused_weights.append(_lowerCamelCase			)

	logger.warning(F"""Unused weights: {unused_weights}"""			)




def __magic_name__	(				_lowerCamelCase:		Union[str, Any],					_lowerCamelCase:		Tuple,					_lowerCamelCase:		Any,					_lowerCamelCase:		List[Any],					_lowerCamelCase:		List[Any]			)					->						Union[str, Any]:

	'''simple docstring'''




	lowerCAmelCase												=  full_name.split('''conv_layers.'''			)[-1]
	lowerCAmelCase												=  name.split('''.'''			)
	lowerCAmelCase												=  int(items[0]			)
	lowerCAmelCase												=  int(items[1]			)

	if type_id == 0:
		if "bias" in name:
			assert value.shape == feature_extractor.conv_layers[layer_id].conv.bias.data.shape, (
			    F"""{full_name} has size {value.shape}, but"""
			    F""" {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found."""
			)
			lowerCAmelCase												=  value
			logger.info(F"""Feat extract conv layer {layer_id} was initialized from {full_name}."""			)
		elif "weight" in name:
			assert value.shape == feature_extractor.conv_layers[layer_id].conv.weight.data.shape, (
			    F"""{full_name} has size {value.shape}, but"""
			    F""" {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found."""
			)
			lowerCAmelCase												=  value
			logger.info(F"""Feat extract conv layer {layer_id} was initialized from {full_name}."""			)
	elif (type_id == 2 and not use_group_norm) or (type_id == 2 and layer_id == 0 and use_group_norm):
		if "bias" in name:
			assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape, (
			    F"""{full_name} has size {value.shape}, but {feature_extractor[layer_id].layer_norm.bias.data.shape} was"""
			    " found."
			)
			lowerCAmelCase												=  value
			logger.info(F"""Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."""			)
		elif "weight" in name:
			assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape, (
			    F"""{full_name} has size {value.shape}, but"""
			    F""" {feature_extractor[layer_id].layer_norm.weight.data.shape} was found."""
			)
			lowerCAmelCase												=  value
			logger.info(F"""Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."""			)
	else:
		unused_weights.append(_lowerCamelCase			)




def __magic_name__	(				_lowerCamelCase:		List[str],					_lowerCamelCase:		int			)					->						List[Any]:

	'''simple docstring'''




	lowerCAmelCase												=  SEWConfig()
	if is_finetuned:
		lowerCAmelCase												=  model.wav_encoder.wav_model.cfg
	else:
		lowerCAmelCase												=  model.cfg

	lowerCAmelCase												=  fs_config.conv_bias
	lowerCAmelCase												=  eval(fs_config.conv_feature_layers			)
	lowerCAmelCase												=  [x[0] for x in conv_layers]
	lowerCAmelCase												=  [x[1] for x in conv_layers]
	lowerCAmelCase												=  [x[2] for x in conv_layers]
	lowerCAmelCase												=  '''gelu'''
	lowerCAmelCase												=  '''layer''' if fs_config.extractor_mode == '''layer_norm''' else '''group'''
	lowerCAmelCase												=  0.0
	lowerCAmelCase												=  fs_config.activation_fn.name
	lowerCAmelCase												=  fs_config.encoder_embed_dim
	lowerCAmelCase												=  0.02
	lowerCAmelCase												=  fs_config.encoder_ffn_embed_dim
	lowerCAmelCase												=  1E-5
	lowerCAmelCase												=  fs_config.encoder_layerdrop
	lowerCAmelCase												=  fs_config.encoder_attention_heads
	lowerCAmelCase												=  fs_config.conv_pos_groups
	lowerCAmelCase												=  fs_config.conv_pos
	lowerCAmelCase												=  len(_lowerCamelCase			)
	lowerCAmelCase												=  fs_config.encoder_layers
	lowerCAmelCase												=  fs_config.squeeze_factor

	# take care of any params that are overridden by the Wav2VecCtc model
	if is_finetuned:
		lowerCAmelCase												=  model.cfg
		lowerCAmelCase												=  fs_config.final_dropout
		lowerCAmelCase												=  fs_config.layerdrop
	lowerCAmelCase												=  fs_config.activation_dropout
	lowerCAmelCase												=  fs_config.mask_prob > 0 or fs_config.mask_channel_prob > 0
	lowerCAmelCase												=  fs_config.attention_dropout
	lowerCAmelCase												=  fs_config.dropout_input
	lowerCAmelCase												=  fs_config.dropout
	lowerCAmelCase												=  fs_config.mask_channel_length
	lowerCAmelCase												=  fs_config.mask_channel_prob
	lowerCAmelCase												=  fs_config.mask_length
	lowerCAmelCase												=  fs_config.mask_prob

	lowerCAmelCase												=  '''Wav2Vec2FeatureExtractor'''
	lowerCAmelCase												=  '''Wav2Vec2CTCTokenizer'''

	return config




@torch.no_grad()
def __magic_name__	(				_lowerCamelCase:		Optional[Any],					_lowerCamelCase:		Dict,					_lowerCamelCase:		str=None,					_lowerCamelCase:		Optional[int]=None,					_lowerCamelCase:		Optional[int]=True			)					->						Optional[Any]:

	'''simple docstring'''




	if is_finetuned:
		lowerCAmelCase							,       lowerCAmelCase							,       lowerCAmelCase												=  fairseq.checkpoint_utils.load_model_ensemble_and_task(
		    [checkpoint_path],					arg_overrides={'''data''': '''/'''.join(dict_path.split('''/'''			)[:-1]			)}			)
	else:
		lowerCAmelCase							,       lowerCAmelCase							,       lowerCAmelCase												=  fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path]			)

	if config_path is not None:
		lowerCAmelCase												=  SEWConfig.from_pretrained(_lowerCamelCase			)
	else:
		lowerCAmelCase												=  convert_config(model[0],					_lowerCamelCase			)
	lowerCAmelCase												=  model[0].eval()

	lowerCAmelCase												=  True if config.feat_extract_norm == '''layer''' else False
	lowerCAmelCase												=  WavaVecaFeatureExtractor(
	    feature_size=1,					sampling_rate=16_000,					padding_value=0,					do_normalize=_lowerCamelCase,					return_attention_mask=_lowerCamelCase,					)

	if is_finetuned:
		if dict_path:
			lowerCAmelCase												=  Dictionary.load(_lowerCamelCase			)

			# important change bos & pad token id since CTC symbol is <pad> and
			# not <s> as in fairseq
			lowerCAmelCase												=  target_dict.pad_index
			lowerCAmelCase												=  target_dict.bos_index
			lowerCAmelCase												=  target_dict.pad_index
			lowerCAmelCase												=  target_dict.bos_index
			lowerCAmelCase												=  target_dict.eos_index
			lowerCAmelCase												=  len(target_dict.symbols			)
			lowerCAmelCase												=  os.path.join(_lowerCamelCase,					'''vocab.json'''			)
			if not os.path.isdir(_lowerCamelCase			):
				logger.error('''--pytorch_dump_folder_path ({}) should be a directory'''.format(_lowerCamelCase			)			)
				return
			os.makedirs(_lowerCamelCase,					exist_ok=_lowerCamelCase			)
			with open(_lowerCamelCase,					'''w''',					encoding='''utf-8'''			) as vocab_handle:
				json.dump(target_dict.indices,					_lowerCamelCase			)
			lowerCAmelCase												=  WavaVecaCTCTokenizer(
			    _lowerCamelCase,					unk_token=target_dict.unk_word,					pad_token=target_dict.pad_word,					bos_token=target_dict.bos_word,					eos_token=target_dict.eos_word,					word_delimiter_token='''|''',					do_lower_case=_lowerCamelCase,					)
			lowerCAmelCase												=  WavaVecaProcessor(feature_extractor=_lowerCamelCase,					tokenizer=_lowerCamelCase			)
			processor.save_pretrained(_lowerCamelCase			)

		lowerCAmelCase												=  SEWForCTC(_lowerCamelCase			)
	else:
		lowerCAmelCase												=  SEWModel(_lowerCamelCase			)
		feature_extractor.save_pretrained(_lowerCamelCase			)

	recursively_load_weights(_lowerCamelCase,					_lowerCamelCase,					_lowerCamelCase			)

	hf_model.save_pretrained(_lowerCamelCase			)


if __name__ == "__main__":
					UpperCAmelCase        =				argparse.ArgumentParser()
					parser.add_argument("""--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the output PyTorch model.""")
					parser.add_argument("""--checkpoint_path""", default=None, type=str, help="""Path to fairseq checkpoint""")
					parser.add_argument("""--dict_path""", default=None, type=str, help="""Path to dict of fine-tuned model""")
					parser.add_argument("""--config_path""", default=None, type=str, help="""Path to hf config.json of model to convert""")
					parser.add_argument(
					    """--is_finetuned""", action="""store_true""", help="""Whether the model to convert is a fine-tuned model or not"""
					)
					UpperCAmelCase        =				parser.parse_args()
					convert_sew_checkpoint(
					    args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, args.is_finetuned
					)




"""simple docstring"""






from collections import namedtuple

UpperCAmelCase        =				namedtuple("""from_to""", """from_ to""")

UpperCAmelCase        =				{
    """cubicmeter""": from_to(1, 1),
    """litre""": from_to(0.001, 1_0_0_0),
    """kilolitre""": from_to(1, 1),
    """gallon""": from_to(0.00_454, 264.172),
    """cubicyard""": from_to(0.76_455, 1.30_795),
    """cubicfoot""": from_to(0.028, 35.3_147),
    """cup""": from_to(0.000_236_588, 4_226.75),
}




def __magic_name__	(				_lowerCamelCase:		float,					_lowerCamelCase:		str,					_lowerCamelCase:		str			)					->						float:

	'''simple docstring'''




	if from_type not in METRIC_CONVERSION:
		raise ValueError(
		    F"""Invalid 'from_type' value: {from_type!r}  Supported values are:\n"""
		    + ''', '''.join(_lowerCamelCase			)			)
	if to_type not in METRIC_CONVERSION:
		raise ValueError(
		    F"""Invalid 'to_type' value: {to_type!r}.  Supported values are:\n"""
		    + ''', '''.join(_lowerCamelCase			)			)
	return value * METRIC_CONVERSION[from_type].from_ * METRIC_CONVERSION[to_type].to


if __name__ == "__main__":
					import doctest

					doctest.testmod()


